mirror of
https://github.com/modelscope/modelscope.git
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101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from modelscope.models.base import TorchModel
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from modelscope.preprocessors import Preprocessor
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from modelscope.utils.regress_test_utils import (compare_arguments_nested,
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numpify_tensor_nested)
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class TorchBaseTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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self.tmp_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(self.tmp_dir):
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os.makedirs(self.tmp_dir)
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def tearDown(self):
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shutil.rmtree(self.tmp_dir)
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super().tearDown()
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def test_custom_model(self):
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class MyTorchModel(TorchModel):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(1, 20, 5)
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self.conv2 = nn.Conv2d(20, 20, 5)
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def forward(self, input):
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x = F.relu(self.conv1(input))
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return F.relu(self.conv2(x))
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model = MyTorchModel()
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model.train()
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model.eval()
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out = model.forward(torch.rand(1, 1, 10, 10))
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self.assertEqual((1, 20, 2, 2), out.shape)
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def test_custom_model_with_postprocess(self):
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add_bias = 200
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class MyTorchModel(TorchModel):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(1, 20, 5)
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self.conv2 = nn.Conv2d(20, 20, 5)
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def forward(self, input):
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x = F.relu(self.conv1(input))
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return F.relu(self.conv2(x))
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def postprocess(self, x):
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return x + add_bias
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model = MyTorchModel()
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model.train()
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model.eval()
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out = model(torch.rand(1, 1, 10, 10))
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self.assertEqual((1, 20, 2, 2), out.shape)
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self.assertTrue(np.all(out.detach().numpy() > (add_bias - 10)))
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def test_save_pretrained(self):
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preprocessor = Preprocessor.from_pretrained(
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'damo/nlp_structbert_sentence-similarity_chinese-tiny')
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model = TorchModel.from_pretrained(
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'damo/nlp_structbert_sentence-similarity_chinese-tiny')
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model.eval()
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with torch.no_grad():
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res1 = numpify_tensor_nested(
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model(**preprocessor(('test1', 'test2'))))
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save_path = os.path.join(self.tmp_dir, 'test_save_pretrained')
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model.save_pretrained(
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save_path, save_checkpoint_names='pytorch_model.bin')
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self.assertTrue(
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os.path.isfile(os.path.join(save_path, 'pytorch_model.bin')))
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self.assertTrue(
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os.path.isfile(os.path.join(save_path, 'configuration.json')))
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self.assertTrue(os.path.isfile(os.path.join(save_path, 'vocab.txt')))
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model = TorchModel.from_pretrained(save_path)
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model.eval()
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with torch.no_grad():
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res2 = numpify_tensor_nested(
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model(**preprocessor(('test1', 'test2'))))
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self.assertTrue(compare_arguments_nested('', res1, res2))
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if __name__ == '__main__':
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unittest.main()
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